基于语言与代码的概率推理实现归纳与探究

中文摘要 AI 辅助

人类如何从稀疏、流式且含噪声的经验数据中生成并维持抽象知识，是认知科学领域长期存在的挑战。任何计算解释都必须满足至少三个要求：一是数据高效且计算高效；二是捕捉不确定性的梯度变化，以支撑智能探究与信息收集；三是具备足够灵活性，可在心理层面表征人类能够学习和思考的无限概念范围。本文提出一种计算模型，通过将符号知识编码为结合自然语言与源代码的心理程序，并使用大语言模型（LLM）引导的贝叶斯学习算法依次推断心理程序，从而满足上述三个特性。在一系列行为研究中，该模型成功复现了人类归纳学习与主动探究的定量特征，如锚定效应、花园路径效应等。相比之下，纯大语言模型和经典贝叶斯模型要么在基础任务上失败，要么无法复现人类行为，要么仅以极高计算成本才能成功。这些结果表明，人类持续扩展知识的一种方式是，在心理层面表征跨越类语言与类程序表征的大量假设，随后修正这些假设以近似贝叶斯更新，同时自下而上的神经机制（LLM）使推理既易于处理又可学习。

英文摘要

How humans grow and maintain abstract knowledge from the sparse, streaming noisy data of experience is a longstanding challenge in cognitive science. Any computational account must satisfy at least three desiderata: It must be (1) data-efficient and compute-efficient, (2) capture gradations of uncertainty to support intelligent inquiry and information gathering, and (3) be flexible enough to mentally represent the endless range of concepts people can learn and think about. Here we introduce a computational model that captures these three properties, by encoding symbolic knowledge as mental programs that combine natural language with source code, and sequentially inferring mental programs using LLM-guided Bayesian learning algorithms. Across a range of behavioral studies this model successfully reproduces quantitative signatures of human inductive learning and active inquiry, such as anchoring, garden-pathing, and other effects. In contrast, pure LLMs and classic Bayesian models either fail at the underlying task, or do not reproduce human behavior, or succeed only at exorbitant computational cost. These results suggest that one way humans continually grow their knowledge is by mentally representing many hypotheses spanning language-like and program-like representations, then revising those hypotheses to approximate Bayesian updates, while a bottom-up neural mechanism (an LLM) makes inference both tractable and learnable.

发表机构

• Cornell University（康奈尔大学）
• Massachusetts Institute of Technology（麻省理工学院）

机构由 AI 辅助整理，请以论文原文为准。